CMU SCS 15-826: Multimedia Databases and Data Mining Lecture #8: Fractals - introduction C. Faloutsos.

CMU SCS

15-826: Multimedia Databases and Data Mining

Lecture #8: Fractals - introduction

C. Faloutsos

CMU SCS

15-826 Copyright: C. Faloutsos (2014) 2

Must-read Material

• Christos Faloutsos and Ibrahim Kamel, Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension, Proc. ACM SIGACT-SIGMOD-SIGART PODS, May 1994, pp. 4-13, Minneapolis, MN.

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Recommended Material

optional, but very useful:

• Manfred Schroeder Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise W.H. Freeman and Company, 1991– Chapter 10: boxcounting method– Chapter 1: Sierpinski triangle

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Outline

Goal: ‘Find similar / interesting things’

• Intro to DB

• Indexing - similarity search

• Data Mining

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Indexing - Detailed outline• primary key indexing• secondary key / multi-key indexing• spatial access methods

– z-ordering– R-trees– misc

• fractals– intro– applications

• text

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Intro to fractals - outline

• Motivation – 3 problems / case studies

• Definition of fractals and power laws

• Solutions to posed problems

• More examples and tools

• Discussion - putting fractals to work!

• Conclusions – practitioner’s guide

• Appendix: gory details - boxcounting plots

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Road end-points of Montgomery county:

•Q1: how many d.a. for an R-tree?

•Q2 : distribution?

•not uniform

•not Gaussian

•no rules??

Problem #1: GIS - points

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Problem #2 - spatial d.m.

Galaxies (Sloan Digital Sky Survey w/ B. Nichol)

- ‘spiral’ and ‘elliptical’ galaxies

(stores and households ...)

- patterns?

- attraction/repulsion?

- how many ‘spi’ within r from an ‘ell’?

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Problem #3: traffic

• disk trace (from HP - J. Wilkes); Web traffic - fit a model

• how many explosions to expect?

• queue length distr.?time

# bytes

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Problem #3: traffic

time

# bytes

Poisson indep., ident. distr

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Problem #3: traffic

time

# bytes


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Problem #3: traffic

time

# bytes


Q: Then, how to generatesuch bursty traffic?

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Common answer:

• Fractals / self-similarities / power laws

• Seminal works from Hilbert, Minkowski, Cantor, Mandelbrot, (Hausdorff, Lyapunov, Ken Wilson, …)

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Road map








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What is a fractal?

= self-similar point set, e.g., Sierpinski triangle:

...zero area;

infinite length!

Dimensionality??

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Definitions (cont’d)

• Paradox: Infinite perimeter ; Zero area!

• ‘dimensionality’: between 1 and 2

• actually: Log(3)/Log(2) = 1.58...

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Dfn of fd:

ONLY for a perfectly self-similar point set:

=log(n)/log(f) = log(3)/log(2) = 1.58

...zero area;

infinite length!

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Intrinsic (‘fractal’) dimension

• Q: fractal dimension of a line?

• A: 1 (= log(2)/log(2)!)

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• A: 1 (= log(2)/log(2)!)

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• Q: dfn for a given set of points?

42

33

24

15

yx

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• A: nn ( <= r ) ~ r^1(‘power law’: y=xâ)

• Q: fd of a plane?• A: nn ( <= r ) ~ r^2fd== slope of (log(nn) vs

log(r) )

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15-826 Copyright: C. Faloutsos (2014)


• Local fractal dimension of point ‘P’?

• A: nnP ( <= r ) ~ r^1

• If this equation holds for several values of r,

• Then, the local fractal dimension of point P:

• Local fd = exp = 1

P

EXPLANATIONS

22

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• Local fractal dimension of point ‘A’?

• A: nnP ( <= r ) ~ r^1

• If this is true for all points of the cloud

• Then the exponent is the global f.d.

• Or simply the f.d.

P

EXPLANATIONS

23

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• Global fractal dimension?• A: if • sumall_P [ nnP ( <= r ) ] ~ r^1• Then: exp = global f.d.

• If this is true for all points of the cloud

• Then the exponent is the global f.d.

• Or simply the f.d.

A

EXPLANATIONS

24

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• Algorithm, to estimate it?Notice• Sumall_P [ nnP (<=r) ] is exactly tot#pairs(<=r)

including ‘mirror’ pairs

EXPLANATIONS

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Sierpinsky triangle

log( r )

log(#pairs within <=r )

1.58

== ‘correlation integral’

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Observations:

• Euclidean objects have integer fractal dimensions – point: 0– lines and smooth curves: 1– smooth surfaces: 2

• fractal dimension -> roughness of the periphery

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Important properties

• fd = embedding dimension -> uniform pointset

• a point set may have several fd, depending on scale

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2-d

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1-d

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0-d

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Road map








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Cross-roads of Montgomery county:

•any rules?

Problem #1: GIS points

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Solution #1

A: self-similarity ->• <=> fractals • <=> scale-free• <=> power-laws

(y=xâ, F=C*r^(-2))• avg#neighbors(<= r )

= r^D

log( r )

log(#pairs(within <= r))

1.51

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Solution #1

A: self-similarity• avg#neighbors(<= r )

~ r^(1.51)

log( r )

log(#pairs(within <= r))

1.51

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Examples:MG county

• Montgomery County of MD (road end-points)

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Examples:LB county

• Long Beach county of CA (road end-points)

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Solution#2: spatial d.m.Galaxies ( ‘BOPS’ plot - [sigmod2000])

log(#pairs)

log(r)

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Solution#2: spatial d.m.

log(r)


spi-spi

spi-ell

ell-ell

- 1.8 slope

- plateau!

- repulsion!

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Spatial d.m.

log(r)


spi-spi

spi-ell

ell-ell

- 1.8 slope

- plateau!

- repulsion!

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Spatial d.m.

r1r2

r1

r2

Heuristic on choosing # of clusters

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Spatial d.m.

log(r)


spi-spi

spi-ell

ell-ell

- 1.8 slope

- plateau!

- repulsion!

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Spatial d.m.

log(r)


spi-spi

spi-ell

ell-ell

- 1.8 slope

- plateau!

-repulsion!!

-duplicates

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Solution #3: traffic

• disk traces: self-similar:

time

#bytes

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Solution #3: traffic

• disk traces (80-20 ‘law’ = ‘multifractal’)

time

#bytes

20% 80%

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80-20 / multifractals20 80

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80-20 / multifractals20

• p ; (1-p) in general

• yes, there are dependencies

80

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More on 80/20: PQRS

• Part of ‘self-* storage’ project [Wang+’02]

time

cylinder#

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More on 80/20: PQRS

• Part of ‘self-* storage’ project [Wang+’02]

p q

r s

q

r s

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Solution#3: traffic

Clarification:

• fractal: a set of points that is self-similar

• multifractal: a probability density function that is self-similar

Many other time-sequences are bursty/clustered: (such as?)

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Example:

• network traffic

http://repository.cs.vt.edu/lbl-conn-7.tar.Z

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Web traffic

• [Crovella Bestavros, SIGMETRICS’96]

1000 sec; 100sec10sec; 1sec

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Tape accesses

time

Tape#1 Tape# N

# tapes needed, to retrieve n records?

(# days down, due to failures / hurricanes / communication noise...)

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Tape accesses

time

Tape#1 Tape# N

# tapes retrieved

# qual. records

50-50 = Poisson

real

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Road map




• More tools and examples




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A counter-intuitive example

• avg degree is, say 3.3• pick a node at random

– guess its degree, exactly (-> “mode”)

degree

count

avg: 3.3

?

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– guess its degree, exactly (-> “mode”)

• A: 1!!

degree

count

avg: 3.3

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- what is the degree you expect it to have?

• A: 1!!• A’: very skewed distr.• Corollary: the mean is

meaningless!• (and std -> infinity (!))

degree

count

avg: 3.3

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Rank exponent R• Power law in the degree distribution

[SIGCOMM99]

internet domains

log(rank)

log(degree)

-0.82

att.com

ibm.com

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More tools

• Zipf’s law

• Korcak’s law / “fat fractals”

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A famous power law: Zipf’s law

• Q: vocabulary word frequency in a document - any pattern?

aaron zoo

freq.

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• Bible - rank vs frequency (log-log)

log(rank)

log(freq)

“a”

“the”

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• Bible - rank vs frequency (log-log)

• similarly, in many other languages; for customers and sales volume; city populations etc etclog(rank)

log(freq)

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•Zipf distr:

freq = 1/ rank

•generalized Zipf:

freq = 1 / (rank)â

log(rank)

log(freq)

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Olympic medals (Sydney):

y = -0.9676x + 2.3054

R2 = 0.9458

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2

Series1

Linear (Series1)

rank

log(#medals)

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Olympic medals (Sydney’00, Athens’04):

log( rank)

log(#medals)

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2

athens

sidney

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TELCO data

# of service units

count ofcustomers

‘best customer’

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SALES data – store#96

# units sold

count of products

“aspirin”

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More power laws: areas – Korcak’s law

Scandinavian lakes

Any pattern?

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More power laws: areas – Korcak’s law

Scandinavian lakes area vs complementary cumulative count (log-log axes)

log(count( >= area))

log(area)

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More power laws: Korcak

Japan islands;

area vs cumulative count (log-log axes) log(area)

log(count( >= area))

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(Korcak’s law: Aegean islands)

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Korcak’s law & “fat fractals”

How to generate such regions?

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Korcak’s law & “fat fractals”Q: How to generate such regions?A: recursively, from a single region

...

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so far we’ve seen:

• concepts:– fractals, multifractals and fat fractals

• tools:– correlation integral (= pair-count plot)– rank/frequency plot (Zipf’s law)– CCDF (Korcak’s law)

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so far we’ve seen:

• concepts:– fractals, multifractals and fat fractals

• tools:– correlation integral (= pair-count plot)– rank/frequency plot (Zipf’s law)– CCDF (Korcak’s law)

sameinfo

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Next:

• More examples / applications

• Practitioner’s guide

• Box-counting: fast estimation of correlation integral


CMU SCS 15-826: Multimedia Databases and Data Mining Lecture #8: Fractals - introduction C. Faloutsos.

Documents

cmu scs

faloutsos slide

distr slide

sierpinski triangle

fractals introduction

traffic time

manfred schroeder fractals

analysis of r